Jupyter notebook for stock and portfolio analysis

2022학년도 2학기 금융시장계량분석 수업자료 및 Jupyter Notebook 실습 파일

2022년 1학기 금융시장빅데이터분석 강의자료 및 jupyter notebook 실습 파일

A Jupyter Notebook for calculating and simulating an effective retirement plan

Notebook to manage specific fundamental investment. Scraping financial statements for automatic valuation.

Creating R-Markdown Presentation

This is a collection of Jupyiter notebooks that I made and used throughout computational finance.

This notebook compares the trend of stock prices vs. crypto currencies using visual comparison and corellation.

Cryptocurrency Dashboard displaying price and volume information for BTC, ETH, BNB using Python, Jupyter Notebooks and the Binance API.

This is a jupyter notebook to extract historical interest rates of each commercial banks in Thailand since 31JAN1996 (31 ม.ค. 2539)

Series of tools for analyzing performance, measuring risk, and constructing equity portfolios. Included is a series of notebooks for demonstrating use cases and aiding in interpretability.

This repository hosts a Jupyter Notebook designed for financial data analysis. It provides tools and examples for analyzing market trends, investment opportunities, or financial forecasts using Python.

This repository hosts a Python notebook analyzing the years needed to achieve statistical significance in investment alpha. It visualizes challenges in proving significant alpha, promoting systematic and index fund investing for long-term stability.

This jupyter notebook project, hosted on Codecademy is concerned with analyzing financial data from two Real Estate Investment Trusts. The project utilizes financial statistics and NumPy in order to draw conclusions about the two companies.

A project for predicting Tata Motors stock prices using LSTM neural networks. It includes historical data from July 1991 to June 2024, with notebooks for model training, testing, and evaluation. The repo features datasets, model files, and performance metrics.

This Jupyter Notebook analyzes NVIDIA (NVDA) stock data to compute risk metrics like Value at Risk (VaR), Expected Shortfall (ES), Sharpe Ratio, Sortino Ratio, Maximum Drawdown, and Beta. It includes visualizations of daily returns and metrics to assess the stock’s risk and performance.

FinMan is an educational suite of tools that can be used to conduct an analysis of financial instruments and their performance. It is comprised of notebooks that will aid in financial analysis and data collection. The goal of the project is to provide a simple UI and automate the process of analyzing data using machine learning

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Notebook for Data Science - Machine Learning

Executable notebooks of market analysis.